FastApi added

README.md

@@ -62,7 +62,7 @@ The primary focus is not just on the model's accuracy, but on building a robust,
 
 The project follows a structured MLOps workflow, which is fully automated by the CI/CD pipeline.
 
-```mermaid
+<pre><code class="language-mermaid">
 graph TD
     A[Start: Push Code to GitHub] --> B{GitHub Actions CI/CD};
     B --> C[CI: Install Dependencies & Run Tests];
@@ -75,7 +75,7 @@ graph TD
         H --> I[4. Log Metrics & Model (MLflow)];
         I --> J[5. Push Model (DVC)];
     end
-```
+</code></pre>
 
 ## ⚙️ Getting Started - Local Setup
 

app.py

@@ -95,4 +95,4 @@ async def predictRoute(payload: ImagePayload):
 # --- RUN THE APP ---
 # This block is for local development. Gunicorn/Uvicorn will run the app in production.
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8001)
+    uvicorn.run(app, host="0.0.0.0", port=8080)

dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.8-slim
+FROM python:3.9
 WORKDIR /app
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt